Online Personalized Preference Learning Method Based on In-Formative Query for Lane Centering Control Trajectory

Abstract

The personalization of autonomous vehicles or advanced driver assistance systems has been a widely researched topic, with many proposals aiming to achieve human-like or driver-imitating methods. However, these approaches rely on an implicit assumption that all drivers prefer the vehicle to drive like themselves, which may not hold true for all drivers. To address this issue, this study proposes an online personalized preference learning method (OPPLM) that utilizes a pairwise comparison group preference query and the Bayesian approach. The proposed OPPLM adopts a two-layer hierarchical structure model based on utility theory to represent driver preferences on the trajectory. To improve the accuracy of learning, the uncertainty of driver query answers is modeled. In addition, informative query and greedy query selection methods are used to improve learning speed. To determine when the driver’s preferred trajectory has been found, a convergence criterion is proposed. To evaluate the effectiveness of the OPPLM, a user study is conducted to learn the driver’s preferred trajectory in the curve of the lane centering control (LCC) system. The results show that the OPPLM can converge quickly, requiring only about 11 queries on average. Moreover, it accurately learned the driver’s favorite trajectory, and the estimated utility of the driver preference model is highly consistent with the subject evaluation score.

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

“…Therefore, the method of aligning the characteristics of ADAS with the driver’s individual naturalistic driving characteristics may result in these characteristics not fully satisfying the drivers’ real preferences. In one study [ 16 ], an online personalized preference learning method was proposed based on driver preference feedback queries and Bayesian approaches, and it could quickly and accurately learn the preferences of most subjects. However, the driver preferences are assumed to be a simple linear function of some fixed driving characteristics, which may not be true.…”

“…In this paper, we focus on the LKA system and present a novel method for developing a Driver-Adaptive Lane-Keeping Assistance (DALKA) system. This method can be employed to initialize the driver preference model in the study [ 16 ], making the driver preference model closer to the real preferences of the drivers. Additionally, the method proposed in this paper can be applied in situations where online preference queries for drivers are not feasible.…”